The following samples did not amplify:
– 07:12 set
– Note: 08:13-24 technically did amplify, but comes up below the lowest point of the standard curve, so technically it is effectively “no amplification”.

Below, is the mean Ct (Cq) and mean copy number (not applicable for XC prophage portal gene, since we don’t have a standard curve developed for this target yet) for each of the samples – sorted by abalone experiment, followed by sample accession number.

DNA was isolated using the QIAamp Fast DNA Stool Kit (Qiagen). Tissues were weighed and briefly homogenized with a disposable pestle in InhibitEX Buffer. Manufacturer’s protocol was followed. DNA was eluted in 100μL of Buffer ATE and quantified on the Roberts Lab Qubit3.0 (ThermoFisher) using 1μL with the Qubit dsDNA Broad Range assay.

I need to identify samples from the 1st and 2nd black abalone experiments in order to run qPCRs on them, using the primers mentioned in the title of this post. However, I only want to use samples that are RLO+. The existing qPCR data is all over the place (in multiple spreadsheets and split across multiple tabs within those spreadsheets) and is a serious pain in the neck to track down.

Here are the three different spreadsheets that I’ve found that have existing withering syndrome RLO (RLO) qPCR data:

I took the time to aggregate all of this data into a somewhat messy spreadsheet that contains the “raw” qPCR data from all of the black abalone qPCR data. I also calculated the mean copy number for all of the replicates:

Additionally, I’ve also added the mean RLO qPCR data to Lisa’s Black Abalone Expt 1 spreadsheet. This spreadsheet is currently the most comprehensive aggregation of black abalone data, since it also contains histology scoring of various tissues, as well as qPCR data for a slew of black abalone genes:

The SELECT statement above selects the columns (Sample, qPCR_date, etc..) from the database (i.e. table) we created earlier and only picks the rows where the value in the “mean_copies” column is greater than or equal to 0. This ensures that only the rows with values are selected and gets rid off all the “mess” that we don’t want in the final spreadsheet.

Change output back to screen (so we don’t continue to write to the csv file we made a few steps ago):